---
title: Improving Project Cost Estimates
url: https://www.ml-quant.com/papers/repec/taf-tprsxx-v-62-y-2024-i-12-p-4372-4388/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:taf:tprsxx:v:62:y:2024:i:12:p:4372-4388
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F00207543.2023.2262051%3Bh%3Drepec%3Ataf%3Atprsxx%3Av%3A62%3Ay%3A2024%3Ai%3A12%3Ap%3A4372-4388
featured: 2024-05-08
citations: unknown
topic: Trading, Microstructure & Execution
---


# Improving Project Cost Estimates

A machine learning model using XGBoost has been developed to enhance the accuracy of project cost forecasting, providing consistent and accurate estimates throughout project execution.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F00207543.2023.2262051%3Bh%3Drepec%3Ataf%3Atprsxx%3Av%3A62%3Ay%3A2024%3Ai%3A12%3Ap%3A4372-4388
- Identifier: RePEc:taf:tprsxx:v:62:y:2024:i:12:p:4372-4388
- Released: 2024-05-08
- First featured: Quant Letter No. 48 (2024-05-08): https://www.ml-quant.com/issues/2024-05-08/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

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